optical fields
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Author(s):  
Randy Lemons ◽  
Sergio Carbajo

Abstract In the context of diffractive optics, phase retrieval is a heavily investigated process of recreating an entire complex electric field from partial amplitude-only information through iterative algorithms. However, existing methods can fall into local minima during reconstructions or struggle to recover unusual and novel electric field distributions. We present a numerical method based on a global-optimization genetic algorithm that reconstructs non-trivial electric field distributions from single diffracted intensity distributions. Diffraction and propagation of the optical fields over arbitrary distances is modeled through implementation of the angular spectrum technique. Additionally, a coherently-locked laser array system is used as an experimental case-study demonstrating $0.09 \pi$ phase reconstruction accuracy of initial laser parameters from single intensity images.


Optics ◽  
2022 ◽  
Vol 3 (1) ◽  
pp. 19-34
Author(s):  
Milo W. Hyde ◽  
Olga Korotkova

Generalizing our prior work on scalar multi-Gaussian (MG) distributed optical fields, we introduce the two-dimensional instantaneous electric-field vector whose components are jointly MG distributed. We then derive the single-point Stokes parameter probability density functions (PDFs) of MG-distributed light having an arbitrary degree and state of polarization. We show, in particular, that the intensity contrast of such a field can be tuned to values smaller or larger than unity. We validate our analysis by generating an example partially polarized MG field with a specified single-point polarization matrix using two different Monte Carlo simulation methods. We then compute the joint PDFs of the instantaneous field components and the Stokes parameter PDFs from the simulated MG fields, while comparing the results of both Monte Carlo methods to the corresponding theory. Lastly, we discuss the strengths, weaknesses, and applicability of both simulation methods in generating MG fields.


2022 ◽  
Vol 105 (1) ◽  
Author(s):  
Jan Peřina ◽  
Pavel Pavlíček ◽  
Václav Michálek ◽  
Radek Machulka ◽  
Ondřej Haderka
Keyword(s):  

2021 ◽  
Author(s):  
Wen Zhao ◽  
Nan Huo ◽  
Liang Cui ◽  
Xiaoying Li ◽  
Zhe-Yu Ou
Keyword(s):  

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruirui Zhang ◽  
Manna Gu ◽  
Rui Sun ◽  
Xiangyu Zeng ◽  
Yuqin Zhang ◽  
...  

Abstract Artificial nanostructures in metasurfaces induce strong spin–orbit interactions (SOIs), by which incident circularly polarized light can be transformed into two opposite spin components. The component with an opposite helicity to the incident light acquires a geometric phase and is used to realize the versatile functions of the metasurfaces; however, the other component, with an identical helicity, is often neglected as a diffused background. Here, by simultaneously manipulating the two spin components originating from the SOI in plasmonic metasurfaces, independent wavefields in the primary and converted spin channels are achieved; the wavefield in the primary channel is controlled by tailoring the dynamic phase, and that in the converted channel is regulated by designing the Pancharatnam–Berry phase in concurrence with the dynamic phase. The scheme is realized by generating optical lattice fields with different topologies in two spin channels, with the metasurfaces composed of metal nanoslits within six round-apertures mimicking the multi-beam interference. This study demonstrates independent optical fields in a dual-spin channel based on the SOI effect in the metasurface, which provides a higher polarization degree of freedom to modify optical properties at the subwavelength scale.


2021 ◽  
Vol 104 (5) ◽  
Author(s):  
Ziheng Xiu ◽  
Yusen Zhang ◽  
Hongsheng Shi ◽  
Hongxia Zheng ◽  
Huajin Chen ◽  
...  

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Shaofu Xu ◽  
Jing Wang ◽  
Haowen Shu ◽  
Zhike Zhang ◽  
Sicheng Yi ◽  
...  

AbstractOptical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.


Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ying Li ◽  
Ya-Feng Jiao ◽  
Jing-Xue Liu ◽  
Adam Miranowicz ◽  
Yun-Lan Zuo ◽  
...  

Abstract The polarizations of optical fields, besides field intensities, provide more degrees of freedom to manipulate coherent light–matter interactions. Here, we propose how to achieve a coherent switch of optomechanical entanglement in a polarized-light-driven cavity system. We show that by tuning the polarizations of the driving field, the effective optomechanical coupling can be well controlled and, as a result, quantum entanglement between the mechanical oscillator and the optical transverse electric mode can be coherently and reversibly switched to that between the same phonon mode and the optical transverse magnetic mode. This ability to switch optomechanical entanglement with such a vectorial device can be important for building a quantum network being capable of efficient quantum information interchanges between processing nodes and flying photons.


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